Elasticsearch Vector Database: Ship in minutes, scale affordably to hundreds of billions
The hard parts of hybrid retrieval, already done, with optimized defaults, third party and native Jina AI models, and managed GPU inference all out of the box. Build fast, scalable AI apps, not infrastructure.
AI shopping agents: Why context comes before the query
AI shopping agents that guess at your vocabulary make expensive mistakes. Pre-computed catalog context stops the guessing before the first tool call.
jina-clip-v2 brings text-to-image search across 89 languages to Elasticsearch, no GPU needed
Run multimodal search across 89 languages inside Elasticsearch with jina-clip-v2: one embedding space for text and images, with no separate model infrastructure to manage.
How we built a persistent agent memory layer on Elasticsearch with 0.89 recall and zero tenant leaks
Discover the architecture behind a persistent, multi-tenant agent memory layer on Elasticsearch: three indices, hybrid retrieval with RRF and a reranker, supersession, decay, and per-user DLS isolation. R@10 0.89 across 168 questions. Full open-source implementation included.
Best practices for building a modern app with vector search
Exploring six vector search tips for building modern AI search applications entirely on Elasticsearch, with an opinionated rationale at each architectural decision.
Cutting agent costs with pre-computed context
Pre-computing context as Knowledge Indicators reduces LLM agent token costs by up to 75% and improves answer accuracy from 60% to 92%. This post covers the extraction, retrieval and feedback loop that make it work, tested against the BrowseComp-Plus benchmark.
How to measure and improve Elasticsearch search recall: from 0.43 to 0.75 with hybrid search
Learn how to measure and improve search recall in Elasticsearch by combining BM25 lexical search with Jina AI vector embeddings, using the rank_eval API to validate the improvement with real numbers.
Entity resolution with Elasticsearch, part 4: The ultimate challenge
Solving and evaluating entity resolution challenges in a highly diverse “ultimate challenge” dataset designed to prevent shortcuts.
Hybrid search with Java: LangChain4j Elasticsearch integration
Learn how to use hybrid search in LangChain4j via its Elasticsearch integrations, with a complete Java example.
Entity resolution with Elasticsearch, part 3: Optimizing LLM integration with function calling
Learn how function calling enhances LLM integration, enabling a reliable and cost-efficient entity resolution pipeline in Elasticsearch.
Entity resolution with Elasticsearch & LLMs, Part 2: Matching entities with LLM judgment and semantic search
Using semantic search and transparent LLM judgment for entity resolution in Elasticsearch.
Ensuring semantic precision with minimum score
Improve semantic precision by employing minimum score thresholds. The article includes concrete examples for semantic and hybrid search.